Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study
We provided a feasibility study of HRV as a potential biomarker for depression. The proposed model based on HRV provides clinicians with a quantitative auxiliary diagnostic tool, which is assist to improving the accuracy and efficiency of depression diagnosis, and can also be utilized for the monitoring and prevention of depression.
Authors
- Minglan Yu
- Yunxuan Xu
- Xiaopeng Yao (ORCID: https://orcid.org/0000-0003-1586-2079)
- Huiqin Zhang (ORCID: https://orcid.org/0000-0002-0698-0252)
- Min Yang (ORCID: https://orcid.org/0000-0002-2589-4966)
- Bo Xiang
Institutions
- Southwest University of Science and Technology (CN)
- Southwest Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
Publication Details
- Journal
- BMC Psychiatry
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12888-024-06384-w
- Citations
- 6
- Primary Topic
- Heart Rate Variability and Autonomic Control
- Type
- article
- Field-Weighted Citation Impact
- 2.41